ee3111-stats/EE3111_LAB2 at main · Worthy-Programmer/ee3111-stats · GitHub

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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"toc_visible":true,"authorship_tag":"ABX9TyMXHR0e2nN4FxATuAAFRdre"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Exploratory Data Analysis & Distribution Fitting\n"],"metadata":{"id":"YpydjTgLZPPR"}},{"cell_type":"markdown","source":["## 1. Part A: Maternal Smoking\n"],"metadata":{"id":"Grxf2BNlZUHN"}},{"cell_type":"code","execution_count":67,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hcY8DAC1Y3_O","executionInfo":{"status":"ok","timestamp":1770812032878,"user_tz":-330,"elapsed":3303,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"9cda10fa-c124-410c-d15f-b02b2aac78c0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["# Access to drive\n","from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","source":["filepath_a = \"/content/drive/MyDrive/Colab Notebooks/EE3111_lab2_A.data\"\n","import pandas as pd\n","\n","df = pd.read_csv(filepath_a, sep=r'\\s+', engine='python') # Here '\\s+' is a regex used for capturing all spaces\n","df.head()\n","df.size"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ukvzgiYlZLqG","executionInfo":{"status":"ok","timestamp":1770812032910,"user_tz":-330,"elapsed":30,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"1eac4b29-ff28-4198-ba73-519c8c5a901f"},"execution_count":68,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2472"]},"metadata":{},"execution_count":68}]},{"cell_type":"code","source":["#df.filter() #Nvm this is for column and index based filtering\n","# Filtering DF\n","df = df[df['smoke'] < 2]\n","df.size"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ijEmaFM1ZIOh","executionInfo":{"status":"ok","timestamp":1770812032914,"user_tz":-330,"elapsed":2,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"6126a962-28a4-44e8-9bf0-301078d5ae3f"},"execution_count":69,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2452"]},"metadata":{},"execution_count":69}]},{"cell_type":"markdown","source":["###1.1. Frequency Table and Proportions"],"metadata":{"id":"8T59ZreCr4UT"}},{"cell_type":"code","source":["print('Min, Max', df['bwt'].min(), df['bwt'].max())\n","\n","bins = range(df['bwt'].min(), df['bwt'].max() + 10, 10)\n","bin_labels = [f\"{bins[i]} - {bins[i+1]}\" for i in range(len(bins)-1)]\n","\n","\n","df['bin_range'] = pd.cut(df['bwt'], bins=bins, labels=bin_labels)\n","\n","freq_table = df.groupby(['bin_range', 'smoke'], observed=True).size().unstack(fill_value=0) # Afaik, this groups other columns by bin_range and smoke by size\n","freq_table.columns = ['Non-Smoker', 'Smoker']\n","freq_table['Total'] = freq_table['Non-Smoker'] + freq_table['Smoker']\n","freq_table['Smoker Percentage %'] = freq_table['Smoker'] * 100 / freq_table['Total']\n","freq_table.round(2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":506},"id":"7-QkeziSr-uV","executionInfo":{"status":"ok","timestamp":1770812032952,"user_tz":-330,"elapsed":37,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"6b293da6-6d83-4c3c-fc63-7be41e0c94f2"},"execution_count":70,"outputs":[{"output_type":"stream","name":"stdout","text":["Min, Max 55 176\n"]},{"output_type":"execute_result","data":{"text/plain":["           Non-Smoker  Smoker  Total  Smoker Percentage %\n","bin_range                                                \n","55 - 65             3       2      5                40.00\n","65 - 75             5      10     15                66.67\n","75 - 85            12      14     26                53.85\n","85 - 95            16      39     55                70.91\n","95 - 105           66      97    163                59.51\n","105 - 115         127      91    218                41.74\n","115 - 125         185     106    291                36.43\n","125 - 135         163      69    232                29.74\n","135 - 145         100      39    139                28.06\n","145 - 155          40      12     52                23.08\n","155 - 165          15       5     20                25.00\n","165 - 175           8       0      8                 0.00\n","175 - 185           1       0      1                 0.00"],"text/html":["\n","  <div id=\"df-b3653008-4d2d-4009-a513-1b6af0b963ad\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Non-Smoker</th>\n","      <th>Smoker</th>\n","      <th>Total</th>\n","      <th>Smoker Percentage %</th>\n","    </tr>\n","    <tr>\n","      <th>bin_range</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>55 - 65</th>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>40.00</td>\n","    </tr>\n","    <tr>\n","      <th>65 - 75</th>\n","      <td>5</td>\n","      <td>10</td>\n","      <td>15</td>\n","      <td>66.67</td>\n","    </tr>\n","    <tr>\n","      <th>75 - 85</th>\n","      <td>12</td>\n","      <td>14</td>\n","      <td>26</td>\n","      <td>53.85</td>\n","    </tr>\n","    <tr>\n","      <th>85 - 95</th>\n","      <td>16</td>\n","      <td>39</td>\n","      <td>55</td>\n","      <td>70.91</td>\n","    </tr>\n","    <tr>\n","      <th>95 - 105</th>\n","      <td>66</td>\n","      <td>97</td>\n","      <td>163</td>\n","      <td>59.51</td>\n","    </tr>\n","    <tr>\n","      <th>105 - 115</th>\n","      <td>127</td>\n","      <td>91</td>\n","      <td>218</td>\n","      <td>41.74</td>\n","    </tr>\n","    <tr>\n","      <th>115 - 125</th>\n","      <td>185</td>\n","      <td>106</td>\n","      <td>291</td>\n","      <td>36.43</td>\n","    </tr>\n","    <tr>\n","      <th>125 - 135</th>\n","      <td>163</td>\n","      <td>69</td>\n","      <td>232</td>\n","      <td>29.74</td>\n","    </tr>\n","    <tr>\n","      <th>135 - 145</th>\n","      <td>100</td>\n","      <td>39</td>\n","      <td>139</td>\n","      <td>28.06</td>\n","    </tr>\n","    <tr>\n","      <th>145 - 155</th>\n","      <td>40</td>\n","      <td>12</td>\n","      <td>52</td>\n","      <td>23.08</td>\n","    </tr>\n","    <tr>\n","      <th>155 - 165</th>\n","      <td>15</td>\n","      <td>5</td>\n","      <td>20</td>\n","      <td>25.00</td>\n","    </tr>\n","    <tr>\n","      <th>165 - 175</th>\n","      <td>8</td>\n","      <td>0</td>\n","      <td>8</td>\n","      <td>0.00</td>\n","    </tr>\n","    <tr>\n","      <th>175 - 185</th>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0.00</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3653008-4d2d-4009-a513-1b6af0b963ad')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-b3653008-4d2d-4009-a513-1b6af0b963ad button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Histogram Plotting"],"metadata":{"id":"8qX28KYJnyZn"}},{"cell_type":"code","source":["# Now I have to plot it\n","\n","\n","# Using seaborn for histograms\n","import seaborn as sbn\n","import matplotlib.pyplot as plt\n","\n","plt.figure(figsize=(10, 6))\n","\n","#JUST MAKING IT MORE NEAT\n","df['Smoking Status'] = df['smoke'].map({0: 'Non-Smoker', 1: 'Smoker'})\n","\n","# # Does not smoke -wrong!!!, this one stacks the data\n","# sbn.histplot(data=df[df['smoke'] == 0], x='bwt', bins=bins, color = 'skyblue', alpha=0.6, label=\"Non Smoking\" )\n","# # Smokes\n","# sbn.histplot(data=df[df['smoke'] ==1], x = 'bwt', bins=bins, color = 'salmon', alpha=0.6, label=\"Smoking\")\n","\n","sbn.histplot(data=df, x='bwt', hue='Smoking Status', bins=bins,  palette={'Non-Smoker': 'skyblue', 'Smoker': 'salmon'},\n","             alpha=0.6,  element=\"bars\", multiple=\"layer\") # 'layer' is very important or else it will stack up\n","\n","plt.title('Smoking vs Non Smoking Histogram of Birth Weight')\n","plt.xlabel('Birth Weight (ounces)')\n","plt.ylabel('Count')\n","plt.xticks(bins)\n","plt.legend(title=\"Maternal Smoking Status\", labels=['Non-Smoking', 'Smoking'])\n","plt.grid(True, alpha=0.7)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"d8-qMqa0dSDy","executionInfo":{"status":"ok","timestamp":1770812033281,"user_tz":-330,"elapsed":328,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"80f33274-0fae-475e-c7f0-cddc90e1018a"},"execution_count":71,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["###1.3. Boxplot"],"metadata":{"id":"3RZwqsQ3VbMo"}},{"cell_type":"code","source":["\n","\n","# Plotting both on the same axes using 'hue'\n","sbn.displot(\n","    data=df,\n","    x=\"bwt\",\n","    hue=\"Smoking Status\",   # This splits the data into two layers\n","    kind=\"hist\",\n","    cumulative=True,\n","    element=\"step\",\n","    fill=False,\n","    common_norm=False    ,   # This ensures BOTH curves reach the 100% mark,\n","    stat=\"density\"\n",")\n","\n","plt.title(\"CDF of Birth Weight by Smoking Status\")\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":529},"id":"7JYcks7YjgZT","executionInfo":{"status":"ok","timestamp":1770812033585,"user_tz":-330,"elapsed":303,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"31eed5d7-ccd8-4a26-d8c5-cc0e30818262"},"execution_count":72,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 635.125x500 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAnkAAAIACAYAAAD+JHqoAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAXjZJREFUeJzt3XlcVPX+x/H3CAwwIriDGoK74l7mXuoNwxZz17IrLi22m0vXLJey0iwz28nKpW5W11KzLM1MzS33pdLct5uCmgsKKAjf3x/+nOsE6DACA4fX8/GYx0PO+Z5zPt+Bgbffc8732IwxRgAAALCUYt4uAAAAALmPkAcAAGBBhDwAAAALIuQBAABYECEPAADAggh5AAAAFkTIAwAAsCBCHgAAgAUR8gAAACyIkIc8l5CQoO7du6tMmTKy2WyaPHlyruy3X79+ioyMvOb9tG3bVvXq1bv2gvLQ/v37ZbPZNH36dI+3nThxYq7XFRkZqTvvvDPX91vQTZ8+XTabTevXr79iu+eee042my2fqgIAV4S8QmDPnj0aOHCgqlatqoCAAAUHB6tVq1Z64403lJKS4mwXGRkpm80mm82mYsWKqWTJkqpfv74efPBBrVmzJst9X2r/91dYWFiu1T948GAtXLhQI0aM0CeffKIOHTpk2/bvdRQvXlxRUVF68cUXlZyc7HENhw8f1nPPPafNmzd7vI+sREVFqWHDhpmWz5kzRzabTW3atMm0burUqbLZbPrhhx9ytZbc8N133+m5557zdhmZHDt2TIMGDVLt2rUVGBio8uXLq2nTpho+fLjOnj3r7fIKrG+++UZt2rRR+fLl5XA4VLVqVfXs2VMLFixwtsmNz0ZB/bkBijpfbxeAK5s/f7569Oghf39/xcbGql69ekpNTdWKFSv01FNP6ffff9eUKVOc7Rs1aqShQ4dKks6cOaPt27dr1qxZ+uCDDzR48GBNmjQp0zHat2+v2NhYl2WBgYG51oeffvpJnTp10rBhw9xqf3k9Z8+e1fLlyzVq1Cht2bJFs2bNcrb74IMPlJGR4dY+Dx8+rOeff16RkZFq1KhRjvuQndatW+ujjz7S6dOnFRIS4ly+cuVK+fr6at26dUpLS5Ofn5/LOh8fH7Vo0cLt40RERCglJcVlP3nhu+++0zvvvFOg/mCfOHFCTZo0UWJiogYMGKDatWvrr7/+0tatW/Xee+/p4YcfVlBQkLfLzNLIkSP19NNPe+XYEydO1FNPPaU2bdpoxIgRcjgc2r17t3788Ud9/vnnzv9s5cZnoyD+3AAg5BVo+/bt0913362IiAj99NNPqlChgnPdo48+qt27d2v+/Pku21SqVEn//Oc/XZZNmDBBvXv31uuvv64aNWro4Ycfdllfs2bNTNvkpqNHj6pkyZJut/97PQ899JBSU1M1e/ZsnTt3TgEBAZLkVuC5cOGC20HQE61bt9YHH3ygVatW6bbbbnMuX7lypXr27KmZM2dqw4YNat68uXPdihUr1KBBA5UoUcLt49hsNme/i5qPPvpIBw8e1MqVK9WyZUuXdYmJibLb7V6q7Op8fX3l65v/v2YvXLigF154Qe3bt89yxPjo0aP5XhOA/Mfp2gLslVde0dmzZ/XRRx+5BLxLqlevrkGDBl11P4GBgfrkk09UunRpvfTSSzLG5Ep9e/fuVY8ePVS6dGk5HA41b97cJXReum7JGKN33nnHeQrWE2FhYbLZbC5/MP9+Td7l155NnjxZ1apVk7+/v959913deOONkqT+/fs76/j79W3btm1Tu3bt5HA4VKlSJb3yyitXrat169aSLoa6S86dO6eNGzeqa9euqlq1qsu6Y8eOaefOnc7tJOnPP//UgAEDFBoaKn9/f9WtW1dTp051OU521+TNmjVLUVFRCggIUL169TRnzpwrXqs4ZcoU5/ty4403at26dS7v5zvvvCPJ9bS5O3744Qc1atRIAQEBioqK0uzZs53r9u7dK5vNptdffz3TdqtWrZLNZtNnn32W7b737NkjHx8fl6B8SXBwsEv4vXR95datW9WmTRs5HA5Vr15dX375pSRp2bJlatasmQIDA1WrVi39+OOPmfa5adMm3XbbbQoODlZQUJBuueUW/fLLL1d9D06ePKmmTZvquuuu044dOyRlfU2ezWbTY489prlz56pevXrO7/nlp1AvWbp0qZo0aaKAgABVq1ZN77//vlvX+R0/flyJiYlq1apVluvLly/v3P+VPhvLly9Xjx49VLlyZfn7+ys8PFyDBw92uUzkSj83S5culc1m09KlS12On9XPc3x8vPr376/rrrtO/v7+qlChgjp16qT9+/dfsa8AssdIXgH2zTffqGrVqplGLzwRFBSkLl266KOPPtK2bdtUt25d57pz587p+PHjLu1LlCghf3//bPeXkJCgli1bKjk5WU888YTKlCmjGTNm6K677tKXX36pLl266Oabb9Ynn3yiPn36ZHlKODuX15OUlKSVK1dqxowZ6t27t1ujItOmTdO5c+f04IMPyt/fX126dNGZM2c0evRoPfjgg7rpppskyeV9PXnypDp06KCuXbuqZ8+e+vLLLzV8+HDVr1/fZYTu76pWraqKFStqxYoVzmXr1q1TamqqWrZsqZYtW2rlypXOU+irVq2S9L9wmJCQoObNmzv/8JcrV07ff/+97rvvPiUmJurJJ5/M9tjz589Xr169VL9+fY0fP14nT57Ufffdp0qVKmXZfubMmTpz5owGDhwom82mV155RV27dtXevXvl5+engQMH6vDhw1q0aJE++eSTq77Pl+zatUu9evXSQw89pL59+2ratGnq0aOHFixYoPbt26tq1apq1aqVPv30Uw0ePNhl208//VQlSpRQp06dst1/RESE0tPT9cknn6hv375XrefkyZO68847dffdd6tHjx567733dPfdd+vTTz/Vk08+qYceeki9e/fWq6++qu7du+vQoUPOUdXff/9dN910k4KDg/Wvf/1Lfn5+ev/999W2bVtnQMzK8ePH1b59e504cULLli1TtWrVrljjihUrNHv2bD3yyCMqUaKE3nzzTXXr1k0HDx5UmTJlJF0Mmx06dFCFChX0/PPPKz09XWPHjlW5cuWu+h6UL19egYGB+uabb/T444+rdOnSWbarU6eOxo4dm+1nY9asWUpOTtbDDz+sMmXKaO3atXrrrbf03//+13nphKc/N3/XrVs3/f7773r88ccVGRmpo0ePatGiRTp48GCu3GAFFEkGBdLp06eNJNOpUye3t4mIiDB33HFHtutff/11I8l8/fXXzmWSsnxNmzbtisd68sknjSSzfPly57IzZ86YKlWqmMjISJOenu5yjEcffdStPmRXT+fOnc25c+dc2vbt29dEREQ4v963b5+RZIKDg83Ro0dd2q5bty7bfrVp08ZIMh9//LFz2fnz501YWJjp1q3bVWvu0aOHCQwMNKmpqcYYY8aPH2+qVKlijDHm3XffNeXLl3e2HTZsmJFk/vzzT2OMMffdd5+pUKGCOX78uMs+7777bhMSEmKSk5Nd+nZ5/fXr1zfXXXedOXPmjHPZ0qVLjaQs35cyZcqYEydOOJd//fXXRpL55ptvnMseffRRk5NfCxEREUaS+eqrr5zLTp8+bSpUqGAaN27sXPb+++8bSWb79u3OZampqaZs2bKmb9++VzxGfHy8KVeunJFkateubR566CEzc+ZMc+rUqUxtL30vZ86c6Vz2xx9/GEmmWLFi5pdffnEuX7hwYab3tHPnzsZut5s9e/Y4lx0+fNiUKFHC3Hzzzc5l06ZNM5LMunXrzJEjR0zdunVN1apVzf79+13qGTNmTKb3U5Kx2+1m9+7dzmVbtmwxksxbb73lXNaxY0fjcDicPyvGGLNr1y7j6+vr1vdo9OjRRpIpXry4ue2228xLL71kNmzYkKndlT4bl37+Ljd+/Hhjs9nMgQMHnMuy+7lZsmSJkWSWLFnisvzvP88nT540ksyrr7561X4BcB+nawuoxMREScrRdVtXc+ni9DNnzrgs79SpkxYtWuTyiomJueK+vvvuOzVt2tTltGNQUJAefPBB7d+/X9u2bfO4zsvr+frrrzVixAgtWLBAvXv3dutUc7du3dwa7bhcUFCQy3WAdrtdTZs21d69e6+6bevWrZWSkqINGzZIksu1Y61atdLRo0e1a9cu57oqVaqoYsWKMsboq6++UseOHWWM0fHjx52vmJgYnT59Whs3bszymIcPH9avv/6q2NhYl5sO2rRpo/r162e5Ta9evVSqVCnn15dGbdzp45VUrFhRXbp0cX4dHBys2NhYbdq0SfHx8ZKknj17KiAgQJ9++qmz3cKFC3X8+PGrXg8aGhqqLVu26KGHHtLJkycVFxen3r17q3z58nrhhRcy/UwEBQXp7rvvdn5dq1YtlSxZUnXq1HEZibv070v9T09P1w8//KDOnTuratWqznYVKlRQ7969tWLFCufn8pL//ve/atOmjdLS0vTzzz8rIiLCrfcsOjraZbSvQYMGCg4Odqnlxx9/VOfOnVWxYkVnu+rVq19xZPlyzz//vGbOnKnGjRtr4cKFevbZZ3XDDTfo+uuv1/bt293ax+U3YCUlJen48eNq2bKljDHatGmTW/tw9zh2u11Lly7VyZMnc22/QFFHyCuggoODJWUOZNfi0lQTfw+O1113naKjo11eWV0DeLkDBw6oVq1amZbXqVPHud5Tl9dz1113ady4cXrxxRc1e/Zsffvtt1fdvkqVKh4d8+/XOZUqVcqtPziXX5dnjNGqVauc10LVq1dPwcHBWrlypc6dO6cNGzY42x87dkynTp3SlClTVK5cOZdX//79JWV/gfyl97d69eqZ1mW1TJIqV66cqX+SrvmPavXq1TO9dzVr1pQk5/VUJUuWVMeOHTVz5kxnm08//VSVKlXSP/7xj6seo0KFCnrvvfd05MgR7dixQ2+++abKlSun0aNH66OPPnJpm9X3MiQkROHh4ZmWSf/r/7Fjx5ScnJztz3VGRoYOHTrksrxPnz46evSoli1blu1p8qz8/Xshuf68HT16VCkpKTn6/mblnnvu0fLly3Xy5En98MMP6t27tzZt2qSOHTvq3LlzV93+4MGD6tevn0qXLq2goCCVK1fOOS3Q6dOn3a7javz9/TVhwgR9//33Cg0N1c0336xXXnnF+Z8EAJ4h5BVQwcHBqlixon777bdc2+elfeXkj0RBccstt0iSfv7556u29WT6Fx8fnyyXuzNy2LBhQ5UoUUIrVqzQH3/8oRMnTjhH8ooVK6ZmzZppxYoVzmv1LoW8S3f9/vOf/8w0knrpld2F8564lj7mhtjYWO3du1erVq3SmTNnNG/ePN1zzz0qVsz9X0M2m001a9bU448/rp9//lnFihVzGR2Usu9nXvS/a9euOnXqlN54440cbZff34vg4GC1b99en376qfr27as9e/ZkO3fmJenp6Wrfvr3mz5+v4cOHa+7cuVq0aJHzZgl37lrP7gaR9PT0TMuefPJJ7dy5U+PHj1dAQIBGjRqlOnXq5OqIIVDUcONFAXbnnXdqypQpWr16dY7mVMvK2bNnNWfOHIWHhztH265FRESE8w7Cy/3xxx/O9bnpwoULkuTxxLd5+dSBS3d+rly5UitWrFBwcLDLKdOWLVvqiy++cIbrSyGvXLlyKlGihNLT0xUdHZ2jY156f3fv3p1pXVbL3OXJ+7R7924ZY1y23blzpyS5XDDfoUMHlStXTp9++qmaNWum5ORk9enTx+Naq1atqlKlSunIkSMe7+Ny5cqVk8PhyPbnulixYplGAx9//HFVr15do0ePVkhISK7NiVe+fHkFBATk+vdXkpo0aaIZM2Y437fsvue//vqrdu7cqRkzZrjcNLVo0aJMbbPbx6XR4lOnTrksz26kv1q1aho6dKiGDh2qXbt2qVGjRnrttdf073//+6r9ApAZI3kF2L/+9S8VL15c999/vxISEjKt37Nnj1sjCCkpKerTp49OnDihZ599NlcCz+233661a9dq9erVzmVJSUmaMmWKIiMjFRUVdc3HuNw333wjSVk+XcIdxYsXl5T5j01uad26tY4dO6Zp06apWbNmLqNTLVu21I4dO/T111+rTJkyzpDt4+Ojbt266auvvspyxPbYsWPZHq9ixYqqV6+ePv74Y5fgu2zZMv36668e98OT9+nw4cOaM2eO8+vExER9/PHHatSokcuTU3x9fXXPPffoP//5j6ZPn6769eurQYMGV93/mjVrlJSUlGn52rVr9ddff2V5etUTPj4+uvXWW/X111+7TNuRkJCgmTNnqnXr1s7LKC43atQoDRs2TCNGjNB7772Xa7VER0dr7ty5Onz4sHP57t279f333191++TkZJfP5uUubX/pfcvue35ptPHy0UVjTJa/c7LbR0REhHx8fDKNwL/77ruZ6v376eNq1aqpRIkSOn/+fJb9AHB1jOQVYNWqVdPMmTPVq1cv1alTx+WJF6tWrdKsWbPUr18/l23+/PNP5/96z549q23btmnWrFmKj4/X0KFDNXDgwFyp7emnn9Znn32m2267TU888YRKly6tGTNmaN++ffrqq69ydAru73bu3OnsQ3Jysn755RfNmDFD1atX93jkp1q1aipZsqTi4uJUokQJFS9eXM2aNfPo+r2sXBqdW716daZZ/y9NkfLLL7+oY8eOLiH75Zdf1pIlS9SsWTM98MADioqK0okTJ7Rx40b9+OOPOnHiRLbHHDdunDp16qRWrVqpf//+OnnypN5++23Vq1fP4xHPG264QZL0xBNPKCYmRj4+Pi43MWSlZs2auu+++7Ru3TqFhoZq6tSpSkhI0LRp0zK1jY2N1ZtvvqklS5ZowoQJbtX0ySef6NNPP1WXLl10ww03yG63a/v27Zo6daoCAgL0zDPP5Lyj2XjxxRe1aNEitW7dWo888oh8fX31/vvv6/z581ecN/HVV1/V6dOn9eijj6pEiRK5Mrn4c889px9++EGtWrXSww8/rPT0dOf392qPIEtOTlbLli3VvHlzdejQQeHh4Tp16pTmzp2r5cuXq3PnzmrcuLGk7D8btWvXVrVq1TRs2DD9+eefCg4O1ldffZXlNZzZ/dyEhISoR48eeuutt2Sz2VStWjV9++23ma413blzp2655Rb17NlTUVFR8vX11Zw5c5SQkHDVnz8AV+CVe3qRIzt37jQPPPCAiYyMNHa73ZQoUcK0atXKvPXWWy7TilyazkKSsdlsJjg42NStW9c88MADZs2aNVnuWzmY3uTv9uzZY7p3725KlixpAgICTNOmTc233357Tce4VP+ll4+Pj7nuuuvMgw8+aBISElzaZjeFSnbTMHz99dcmKirKOQXFpekb2rRpY+rWrZup/d/3fyVJSUnO/f7www+Z1jdo0MBIMhMmTMi0LiEhwTz66KMmPDzc+Pn5mbCwMHPLLbeYKVOmZOrb36e5+Pzzz03t2rWNv7+/qVevnpk3b57p1q2bqV27dqZts3pfJJkxY8Y4v75w4YJ5/PHHTbly5YzNZrvqVB2Xpu1ZuHChadCggfH39ze1a9c2s2bNynabunXrmmLFipn//ve/V9z3JVu3bjVPPfWUuf76603p0qWNr6+vqVChgunRo4fZuHGjS9vsvpfZTS+U1c/mxo0bTUxMjAkKCjIOh8O0a9fOrFq1yqXN5VOoXJKenm7uuece4+vra+bOnWuMyX4Klaw+DxEREZmmk1m8eLFp3Lixsdvtplq1aubDDz80Q4cONQEBAVm8U/+TlpZmPvjgA9O5c2cTERFh/P39jcPhMI0bNzavvvqqOX/+vEv77D4b27ZtM9HR0SYoKMiULVvWPPDAA87pXi7/WbzSz82xY8dMt27djMPhMKVKlTIDBw40v/32m8s+jh8/bh599FFTu3ZtU7x4cRMSEmKaNWtm/vOf/1yxnwCuzGZMPl11DSBfNGrUSOXKlcvy2qmCoHHjxipdurQWL17s7VIKpc6dO+v33393TssDANnhmjygkEpLS3PekHLJ0qVLtWXLFrVt29Y7RV3F+vXrtXnzZrefflLUXf74MOni00W+++67Avv9BVCwMJIHFFL79+9XdHS0/vnPf6pixYr6448/FBcXp5CQEP3222/Ox2MVBL/99ps2bNig1157TcePH9fevXtdnjmLrFWoUEH9+vVT1apVdeDAAb333ns6f/68Nm3apBo1ani7PAAFHDdeAIVUqVKldMMNN+jDDz/UsWPHVLx4cd1xxx16+eWXC1TAk6Qvv/xSY8eOVa1atfTZZ58R8NzUoUMHffbZZ4qPj5e/v79atGihcePGEfAAuIWRPAAAAAvimjwAAAALIuQBAABYUJELecYYJSYm5tvzOgEAALyhyIW8M2fOKCQkRGfOnPF2KQAAAHmmyIU8AACAooCQBwAAYEGEPAAAAAsi5AEAAFgQIQ8AAMCCCHkAAAAWRMgDAACwIEIeAACABRHyAAAALIiQBwAAYEGEPAAAAAsi5AEAAFgQIQ8AAMCCCHkAAAAWRMgDAACwIK+GvJ9//lkdO3ZUxYoVZbPZNHfu3Ktus3TpUl1//fXy9/dX9erVNX369DyvEwAAoLDxashLSkpSw4YN9c4777jVft++fbrjjjvUrl07bd68WU8++aTuv/9+LVy4MI8rBQAAKFxsxhjj7SIkyWazac6cOercuXO2bYYPH6758+frt99+cy67++67derUKS1YsMCt4yQmJiokJESnT59WcHDwtZYNAEWPMVJasrercGGMUUpahkfbBjqCZCvG1UuwHl9vF5ATq1evVnR0tMuymJgYPfnkk9luc/78eZ0/f975dWJiYl6VBwDWZ4w0NUY6tMbblTgZI3VPHaMNppZH228beZMcQfynH9ZTqP7rEh8fr9DQUJdloaGhSkxMVEpKSpbbjB8/XiEhIc5XeHh4fpQKANaUllygAp4kpcjf44AHWFmhGsnzxIgRIzRkyBDn14mJiQQ9AMgNw3ZLdkee7Donp1+TU9OlV1ZJktb/q6Ucdp8cHSvQEZTj+oDCoFCFvLCwMCUkJLgsS0hIUHBwsAIDA7Pcxt/fX/7+/vlRHgAUDHl5zVzqZfu1OyR7cTdLMkpJS3ezrdQjbrW2Hcn55TWOoBJy2AvVnzYgzxSqT0KLFi303XffuSxbtGiRWrRo4aWKAKCAKZDXzBl1j1utDQdO5ulxmkSUUqBfzkbxACvzasg7e/asdu/e7fx637592rx5s0qXLq3KlStrxIgR+vPPP/Xxxx9Lkh566CG9/fbb+te//qUBAwbop59+0n/+8x/Nnz/fW10AgIIlH66ZM0ZKua61ZOxS6oWrtk9OTfco4EVVCNash1rIZnOvfaCfj2zuNgaKAK+GvPXr16tdu3bOry9dO9e3b19Nnz5dR44c0cGDB53rq1Spovnz52vw4MF64403dN111+nDDz9UTExMvtcOAAVeHlwzZ4xR9w83acOeRGnMDznefv3IaLevmSO0AdfGqyGvbdu2utI0fVk9zaJt27batGlTHlYFABaRg2vm3JWSekEbDnk2FVWTiFIqU9xOcAPySaG6Jg8AUHDkZFROYmQOyG+EPAAo6HJyt2xqzu+qzcmdr8mp/2vnsPtwJytQgPHpBICCLI/vls2vO18B5L9C9cQLAChyPL1bNry55Hf1my5S0jy785XpSoCCj5E8AMhPOZ2o+PLTr27eLWuMUYr8JTdOwV5++pU7XwFrIeQBQH651lOvbtwtey2nX7nGDrAWTtcCQH65lomKOf0KIIf4LxsAeENOJyr2c8jtRz/8P06/AkUbIQ8AvCEPJir+O06/AkUbp2sBAAAsiJAHAABgQYzjA0AB5+kTKQAUbYQ8ACjAeCIFAE9xuhYACjCmRAHgKUbyAKCQYEoUADlByAOAQoIpUQDkBL8tAOBa5ORZtKk5eGYtAFwjQh4AeOpan0ULAHmIGy8AwFOePovWzefQAsC1YCQPAHKDm8+iNcYoRf4S894ByGOEPADIDW48i5Y57wDkJ07XAkA+8XTOO4l57wDkHCN5AOAFOZnzTmLeOwA5R8gDAC9gzjsAeY3TtQAAABZEyAMAALAgQh4AAIAFEfIAAAAsiKt+AeByPIsWgEUQ8gDgEp5FC8BCOF0LAJfwLFoAFsJIHgBkxc1n0Uq6GPCYqBhAAUPIA4CsuPEsWgAoyDhdCwAAYEGM5AHANTDGKCUt3a22yanutQOA3EDIAwAPGWPUPW61Nhw46e1SACATTtcCgIdS0tI9CnhNIkop0M8nDyoCgP9hJA8AcsH6kdFy2N0LboF+PrJxNy6APEbIA2BdOXl6hXRNT7Bw2H3ksPMrFUDBwW8kANbE0ysAFHFckwfAmjx9eoXEEywAWAIjeQCsLydPr5B4ggUASyDkAbA+nl4BoAjidC0AAIAFEfIAAAAsiJAHAABgQYQ8AAAACyLkAQAAWBAhDwAAwIKYQgUALmOMUUpaulttk1PdawcA3kDIA4D/Z4xR97jV2nDgpLdLAYBrxulaAPh/KWnpHgW8JhGlFOjnkwcVAYDnGMkDgCysHxkth9294Bbo5yMbj0EDUMAQ8gAUHsZIacnutU11s102HHYfOez8igRQePEbDEDhYIw0NUY6tMbblQBAocA1eQAKh7RkzwJeeHPJz5H79QBAAcdIHoDCZ9huye5mcPNzSFwvB6AIIuQBKHzsDsle3NtVAECBxulaAAAACyLkAQAAWBAhDwAAwIIIeQAAABZEyAMAALAgQh4AAIAFEfIAAAAsiJAHAABgQYQ8AAAACyLkAQAAWBAhDwAAwIIIeQAAABbk9ZD3zjvvKDIyUgEBAWrWrJnWrl17xfaTJ09WrVq1FBgYqPDwcA0ePFjnzp3Lp2oBAAAKB6+GvC+++EJDhgzRmDFjtHHjRjVs2FAxMTE6evRolu1nzpypp59+WmPGjNH27dv10Ucf6YsvvtAzzzyTz5UDAAAUbF4NeZMmTdIDDzyg/v37KyoqSnFxcXI4HJo6dWqW7VetWqVWrVqpd+/eioyM1K233qp77rnnqqN/AAAARY3XQl5qaqo2bNig6Ojo/xVTrJiio6O1evXqLLdp2bKlNmzY4Ax1e/fu1Xfffafbb789X2oGAAAoLHy9deDjx48rPT1doaGhLstDQ0P1xx9/ZLlN7969dfz4cbVu3VrGGF24cEEPPfTQFU/Xnj9/XufPn3d+nZiYmDsdAAAAKMC8fuNFTixdulTjxo3Tu+++q40bN2r27NmaP3++XnjhhWy3GT9+vEJCQpyv8PDwfKwYAADAO7w2kle2bFn5+PgoISHBZXlCQoLCwsKy3GbUqFHq06eP7r//fklS/fr1lZSUpAcffFDPPvusihXLnFlHjBihIUOGOL9OTEwk6AEAAMvz2kie3W7XDTfcoMWLFzuXZWRkaPHixWrRokWW2yQnJ2cKcj4+PpIkY0yW2/j7+ys4ONjlBQAAYHVeG8mTpCFDhqhv375q0qSJmjZtqsmTJyspKUn9+/eXJMXGxqpSpUoaP368JKljx46aNGmSGjdurGbNmmn37t0aNWqUOnbs6Ax7AAAA8HLI69Wrl44dO6bRo0crPj5ejRo10oIFC5w3Yxw8eNBl5G7kyJGy2WwaOXKk/vzzT5UrV04dO3bUSy+95K0uAAAAFEg2k915TotKTExUSEiITp8+zalboDBJTZLGVbz472cOS/biuX6I5NQLihq9UJK0bWyMHHav/j8YAK4Jv8EAWJYxRilp6W63T051vy0AFHSEPACWZIxR97jV2nDgpLdLAQCvIOQB8B5jpLRk99qmutnu/6WkpXsc8JpElFKgHzdzASjcCHkAvMMYaWqMdGhNnh9q/choOezuh7ZAPx/ZbLY8rAgA8h4hD4B3pCV7FvDCm0t+jhxt4rD7cBMFgCKH33oAvG/YbsnuZnDzc0iMsgHAVRHyAHif3ZEnU6IAQFHmtceaAQAAIO8Q8gAAACyIkAcAAGBBhDwAAAALIuQBAABYECEPAADAggh5AAAAFkTIAwAAsCBCHgAAgAUR8gAAACyIkAcAAGBBhDwAAAALIuQBAABYECEPAADAggh5AAAAFkTIAwAAsCBCHgAAgAUR8gAAACyIkAcAAGBBhDwAAAALIuQBAABYkK+3CwBgEcZIacnut0/NQVsAQI4R8gBcO2OkqTHSoTXergQA8P8IeQCuXVqy5wEvvLnk53CrqTFGKWnpbrVNTnWvHQBYFSEPQO4atluyuxfaJF0MeDbbVZsZY9Q9brU2HDh5DcUBQNFByAOQu+wOyV4813ebkpbuUcBrElFKgX4+uV4PABR0hDwAhc76kdFy2N0LboF+PrK5MVIIAFZDyANQ6DjsPnLY+fUFAFfCPHkAAAAWRMgDAACwIEIeAACABRHyAAAALIiQBwAAYEGEPAAAAAsi5AEAAFgQIQ8AAMCCCHkAAAAWRMgDAACwIEIeAACABRHyAAAALIiQBwAAYEGEPAAAAAsi5AEAAFgQIQ8AAMCCCHkAAAAWRMgDAACwIEIeAACABRHyAAAALIiQBwAAYEGEPAAAAAsi5AEAAFgQIQ8AAMCCCHkAAAAWRMgDAACwIEIeAACABRHyAAAALMjX2wUAKKCMkdKS3Wub6mY7AEC+IeQByMwYaWqMdGiNtysBAHiI07UAMktL9izghTeX/By5Xw8AIMcYyQNwZcN2S3Y3g5ufQ7LZ8rYeAIBbCHkArszukOzFvV0FACCHOF0LAABgQYQ8AAAAC+J0LQCvMcYoJS3drbbJqe61AwBc5PWQ98477+jVV19VfHy8GjZsqLfeektNmzbNtv2pU6f07LPPavbs2Tpx4oQiIiI0efJk3X777flYNYBrZYxR97jV2nDgpLdLAQBL8mrI++KLLzRkyBDFxcWpWbNmmjx5smJiYrRjxw6VL18+U/vU1FS1b99e5cuX15dffqlKlSrpwIEDKlmyZP4XD+CapKSlexTwmkSUUqCfTx5UBADW4tWQN2nSJD3wwAPq37+/JCkuLk7z58/X1KlT9fTTT2dqP3XqVJ04cUKrVq2Sn5+fJCkyMjI/SwaQB9aPjJbD7l5wC/TzkY1pWgDgqrx240Vqaqo2bNig6Ojo/xVTrJiio6O1evXqLLeZN2+eWrRooUcffVShoaGqV6+exo0bp/T07K/VOX/+vBITE11eAAoWh91HDruvWy8CHgC4x2sh7/jx40pPT1doaKjL8tDQUMXHx2e5zd69e/Xll18qPT1d3333nUaNGqXXXntNL774YrbHGT9+vEJCQpyv8PDwXO0HAABAQVSoplDJyMhQ+fLlNWXKFN1www3q1auXnn32WcXFxWW7zYgRI3T69Gnn69ChQ/lYMQAAgHd47Zq8smXLysfHRwkJCS7LExISFBYWluU2FSpUkJ+fn3x8/nftTp06dRQfH6/U1FTZ7fZM2/j7+8vf3z93iwcAACjgvDaSZ7fbdcMNN2jx4sXOZRkZGVq8eLFatGiR5TatWrXS7t27lZGR4Vy2c+dOVahQIcuABwAAUFR59XTtkCFD9MEHH2jGjBnavn27Hn74YSUlJTnvto2NjdWIESOc7R9++GGdOHFCgwYN0s6dOzV//nyNGzdOjz76qLe6AAAAUCB5dQqVXr166dixYxo9erTi4+PVqFEjLViwwHkzxsGDB1Ws2P9yaHh4uBYuXKjBgwerQYMGqlSpkgYNGqThw4d7qwsAAAAFks0YY7xdRH5KTExUSEiITp8+reDgYG+XAxRMqUnSuIoX//3MYclePNcPkZx6QVGjF0qSto2NkcPu9QfwAICleHS6du/evbldBwAAAHKRRyGvevXqateunf7973/r3LlzuV0TAAAArpFHIW/jxo1q0KCBhgwZorCwMA0cOFBr167N7doAAADgIY9CXqNGjfTGG2/o8OHDmjp1qo4cOaLWrVurXr16mjRpko4dO5bbdQIAACAHrmkKFV9fX3Xt2lWzZs3ShAkTtHv3bg0bNkzh4eGKjY3VkSNHcqtOAAAA5MA1hbz169frkUceUYUKFTRp0iQNGzZMe/bs0aJFi3T48GF16tQpt+oEAABADngU8iZNmqT69eurZcuWOnz4sD7++GMdOHBAL774oqpUqaKbbrpJ06dP18aNG3O7XgAAUAjYbDbNnTs32/WRkZGaPHlyvtVTFHkU8t577z317t1bBw4c0Ny5c3XnnXe6TFosSeXLl9dHH32UK0UCAICcO3bsmB5++GFVrlxZ/v7+CgsLU0xMjFauXOnt0rRu3To9+OCDeX6cLVu26K677lL58uUVEBCgyMhI9erVS0ePHpUkLV26VDabTadOncrRfvfv3y+bzabNmzfnftG5xKPZRxctWqTKlStnCnbGGB06dEiVK1eW3W5X3759c6VIAACQc926dVNqaqpmzJihqlWrKiEhQYsXL9Zff/3l7dJUrly5PD/GsWPHdMstt+jOO+/UwoULVbJkSe3fv1/z5s1TUlJSnh/f2zwayatWrZqOHz+eafmJEydUpUqVay4KAABcm1OnTmn58uWaMGGC2rVrp4iICDVt2lQjRozQXXfd5Wxns9n0/vvv684775TD4VCdOnW0evVq7d69W23btlXx4sXVsmVL7dmzx2X/7733nqpVqya73a5atWrpk08+uWI9Y8aMUYUKFbR161ZJmU/X2mw2ffjhh+rSpYscDodq1KihefPmuexj3rx5qlGjhgICAtSuXTvNmDHjiqNwK1eu1OnTp/Xhhx+qcePGqlKlitq1a6fXX39dVapU0f79+9WuXTtJUqlSpWSz2dSvXz9J0oIFC9S6dWuVLFlSZcqU0Z133unyHlzKO40bN5bNZlPbtm0lSW3bttWTTz7pUkfnzp2d+5Wkd99919mP0NBQde/e/Yrvnac8CnnZPQnt7NmzCggIuKaCAADAtQsKClJQUJDmzp2r8+fPX7HtCy+8oNjYWG3evFm1a9dW7969NXDgQI0YMULr16+XMUaPPfaYs/2cOXM0aNAgDR06VL/99psGDhyo/v37a8mSJZn2bYzR448/ro8//ljLly9XgwYNsq3j+eefV8+ePbV161bdfvvtuvfee3XixAlJ0r59+9S9e3d17txZW7Zs0cCBA/Xss89esV9hYWG6cOGC5syZk2V2CQ8P11dffSVJ2rFjh44cOaI33nhDkpSUlKQhQ4Zo/fr1Wrx4sYoVK6YuXbooIyNDkpzzA//44486cuSIZs+efcVaLlm/fr2eeOIJjR07Vjt27NCCBQt08803u7VtTuXodO2QIUMkXUzbo0ePlsPhcK5LT0/XmjVr1KhRo1wtEAAA5Jyvr6+mT5+uBx54QHFxcbr++uvVpk0b3X333ZmCVv/+/dWzZ09J0vDhw9WiRQuNGjVKMTExkqRBgwapf//+zvYTJ05Uv3799Mgjj0i6mA9++eUXTZw40TkyJkkXLlzQP//5T23atEkrVqxQpUqVrlhzv379dM8990iSxo0bpzfffFNr165Vhw4d9P7776tWrVp69dVXJUm1atXSb7/9ppdeeinb/TVv3lzPPPOMevfurYceekhNmzbVP/7xD8XGxio0NFQ+Pj4qXbq0pIv3EpQsWdK5bbdu3Vz2NXXqVJUrV07btm1TvXr1nKeby5Qpo7CwsCv263IHDx5U8eLFdeedd6pEiRKKiIhQ48aN3d4+J3I0krdp0yZt2rRJxhj9+uuvzq83bdqkP/74Qw0bNtT06dPzpFAAAJAz3bp10+HDhzVv3jx16NBBS5cu1fXXX5/pb/XloS80NFSSVL9+fZdl586dU2JioiRp+/btatWqlcs+WrVqpe3bt7ssGzx4sNasWaOff/75qgHv73UUL15cwcHBzhskduzYoRtvvNGlfdOmTa+6z5deeknx8fGKi4tT3bp1FRcXp9q1a+vXX3+94na7du3SPffco6pVqyo4OFiRkZGSLoa0a9G+fXtFRESoatWq6tOnjz799FMlJydf0z6zk6OQt2TJEi1ZskR9+/bV999/7/x6yZIlWrhwod5//33VqFEjTwoFAAA5FxAQoPbt22vUqFFatWqV+vXrpzFjxri08fPzc/7bZrNlu+zSqUp3tW/fXn/++acWLlzoVvvLj3npuDk9ZlbKlCmjHj16aOLEidq+fbsqVqyoiRMnXnGbjh076sSJE/rggw+0Zs0arVmzRpKUmpp6xe2KFSuW6dRwWlqa898lSpTQxo0b9dlnn6lChQoaPXq0GjZsmOO7e93h0TV506ZNU3BwcG7XAgAA8lhUVNQ131lap06dTNOwrFy5UlFRUS7L7rrrLs2cOVP333+/Pv/882s6Zq1atbR+/XqXZevWrcvxfux2u6pVq+Z8D+x2u6SLl51d8tdff2nHjh0aOXKkbrnlFtWpU0cnT57MtJ+/byddvGv48id+paen67fffnNp4+vrq+joaL3yyivaunWr9u/fr59++inHfbkat6/J69q1q6ZPn67g4GB17dr1im3dvfgQAADkjb/++ks9evTQgAED1KBBA5UoUULr16/XK6+8cs1PpHrqqafUs2dPNW7cWNHR0frmm280e/Zs/fjjj5nadunSRZ988on69OkjX19fj+8kHThwoCZNmqThw4frvvvu0+bNm52nnS+NNP7dt99+q88//1x33323atasKWOMvvnmG3333XeaNm2aJCkiIkI2m03ffvutbr/9dgUGBqpUqVIqU6aMpkyZogoVKujgwYN6+umnXfZdvnx5BQYGasGCBbruuusUEBCgkJAQ/eMf/9CQIUM0f/58VatWTZMmTXIZpfv222+1d+9e3XzzzSpVqpS+++47ZWRkqFatWh69L1fidsgLCQlxvokhISG5XggAAMg9QUFBatasmV5//XXt2bNHaWlpCg8P1wMPPKBnnnnmmvbduXNnvfHGG5o4caIGDRqkKlWqaNq0ac5pRP6ue/fuysjIUJ8+fVSsWLGrDhZlpUqVKvryyy81dOhQvfHGG2rRooWeffZZPfzww/L3989ym6ioKDkcDg0dOlSHDh2Sv7+/atSooQ8//FB9+vSRJFWqVEnPP/+8nn76afXv31+xsbGaPn26Pv/8cz3xxBOqV6+eatWqpTfffNOlf76+vnrzzTc1duxYjR49WjfddJOWLl2qAQMGaMuWLYqNjZWvr68GDx7scjNKyZIlNXv2bD333HM6d+6catSooc8++0x169bN8XtyNTaT3XwoFpWYmKiQkBCdPn2aU85AdlKTpHEVL/77mcOSvXiuHyI59YKiRl+8Tmfb2Bg57B7NzQ6gCHvppZcUFxenQ4cOebuUAsmj36opKSkyxjinUDlw4IDmzJmjqKgo3XrrrblaIAAAgHRxEuEbb7xRZcqU0cqVK/Xqq6+6zN8HVx6FvE6dOqlr16566KGHdOrUKTVt2lR2u13Hjx/XpEmT9PDDD+d2nQAAoIjbtWuXXnzxRZ04cUKVK1fW0KFDNWLECG+XVWB5dHftxo0bddNNN0mSvvzyS4WFhenAgQP6+OOP9eabb+ZqgQAAAJL0+uuv6/Dhwzp37px27typUaNGydeXSz2y41HIS05OVokSJSRJP/zwg7p27apixYqpefPmOnDgQK4WCAAAgJzzKORVr15dc+fO1aFDh7Rw4ULndXhHjx7lZgagoDLm4g0Vbr3yZvZ1AED+8WiMc/To0erdu7cGDx6sW265RS1atJB0cVQvr56/BuAaGCNNjZEOrfF2JQCAfOJRyOvevbtat26tI0eOqGHDhs7lt9xyi7p06ZJrxQHIJWnJngW88OaSnyP36wEA5DmPr1YMCwtTWFiYyzJ3HhQMwMuG7ZbsbgY3P4eUzUzyAICCzaOQl5SUpJdfflmLFy/W0aNHMz08eO/evblSHIA8YHfkyeTGAICCxaOQd//992vZsmXq06ePKlSokO0z4wAAAIqa5557TnPnztXmzZu9WodHIe/777/X/Pnz1apVq9yuBwAAWES/fv00Y8YMjR8/Xk8//bRz+dy5c9WlSxfl9ZNV58yZowkTJmj79u3KyMhQ5cqV1b59e02ePDlPj1tQeDSFSqlSpVS6dOncrgUAAFhMQECAJkyYoJMnT+brcRcvXqxevXqpW7duWrt2rTZs2KCXXnpJaWlp+VpHbkhPT890aZw7PAp5L7zwgkaPHq3kZObSAgAA2YuOjlZYWJjGjx+fbZuvvvpKdevWlb+/vyIjI/Xaa6+5rI+MjNS4ceM0YMAAlShRQpUrV9aUKVOueNxvvvlGrVq10lNPPaVatWqpZs2a6ty5s9555x1nm+eee06NGjXS1KlTVblyZQUFBemRRx5Renq6XnnlFYWFhal8+fJ66aWXXPZ98OBBderUSUFBQQoODlbPnj2VkJCQbS179uxR1apV9dhjj8kYo/Pnz2vYsGGqVKmSihcvrmbNmmnp0qXO9tOnT1fJkiU1b948RUVFyd/fXwcPHrxif7Pi0ena1157TXv27FFoaKgiIyPl5+fnsn7jxo2e7BZAIWaMUUpautvtk1PdbwvAVU4/b7kl0M8nx9fh+/j4aNy4cerdu7eeeOIJXXfddS7rN2zYoJ49e+q5555Tr169tGrVKj3yyCMqU6aM+vXr52z32muv6YUXXtAzzzyjL7/8Ug8//LDatGmjWrVqZXncsLAwzZw5U7/99pvq1auXbX179uzR999/rwULFmjPnj3q3r279u7dq5o1a2rZsmVatWqVBgwYoOjoaDVr1kwZGRnOgLds2TJduHBBjz76qHr16uUS1C7ZunWrYmJidN999+nFF1+UJD322GPatm2bPv/8c1WsWFFz5sxRhw4d9Ouvv6pGjRqSLj5dbMKECfrwww9VpkwZlS9fPkfvu+RhyOvcubMnmwGwKGOMuset1oYD+Xs6BiiqUtLSFTV6Yb4fd9vYGDnsOY8OXbp0UaNGjTRmzBh99NFHLusmTZqkW265RaNGjZIk1axZU9u2bdOrr77qEvJuv/12PfLII5Kk4cOH6/XXX9eSJUuyDXmPP/64li9frvr16ysiIkLNmzfXrbfeqnvvvVf+/v7OdhkZGZo6dapKlCihqKgotWvXTjt27NB3332nYsWKqVatWpowYYKWLFmiZs2aafHixfr111+1b98+hYeHS5I+/vhj1a1bV+vWrdONN97o3PeqVat055136tlnn9XQoUMlXRwFnDZtmg4ePKiKFStKkoYNG6YFCxZo2rRpGjdunCQpLS1N7777rst8xDnlUcgbM2aMxwcEYD0paekeB7wmEaUU6OeTyxUBKGgmTJigf/zjHxo2bJjL8u3bt6tTp04uy1q1aqXJkycrPT1dPj4Xfz80aNDAud5msyksLExHjx6VJN12221avny5JCkiIkK///67ihcvrvnz52vPnj1asmSJfvnlFw0dOlRvvPGGVq9eLYfj4nyhkZGRKlGihHPfoaGh8vHxUbFixVyWXTrW9u3bFR4e7gx4khQVFaWSJUtq+/btzpB38OBBtW/fXi+99JKefPJJZ9tff/1V6enpqlmzpkufz58/rzJlyji/ttvtLn32hMeTIZ86dUpffvml9uzZo6eeekqlS5fWxo0bFRoaqkqVKl1TUQAKr/Ujo+Wwux/aPDn9AxR1gX4+2jY2xivH9dTNN9+smJgYjRgxwmWEzl1/vzTMZrM5b0b48MMPlZKSkmW7atWqqVq1arr//vv17LPPqmbNmvriiy/Uv3//bPd7pWO5q1y5cqpYsaI+++wzDRgwQMHBwZKks2fPysfHRxs2bHAG2EuCgoKc/w4MDLzm340ehbytW7cqOjpaISEh2r9/vx544AGVLl1as2fP1sGDB/Xxxx9fU1EACi+H3cej0zkA3Gez2Qrl5+zll19Wo0aNXE6x1qlTRytXrnRpt3LlStWsWTNTCMqOu4NLkZGRcjgcSkpKcr/ov6lTp44OHTqkQ4cOOUfztm3bplOnTikqKsrZLjAwUN9++61uv/12xcTE6IcfflCJEiXUuHFjpaen6+jRo7rppps8rsMdHt1dO2TIEPXr10+7du1SQECAc/ntt9+un3/+OdeKAwAA1lG/fn3de++9evPNN53Lhg4dqsWLF+uFF17Qzp07NWPGDL399tuZTuvm1HPPPad//etfWrp0qfbt26dNmzZpwIABSktLU/v27T3eb3R0tLMfGzdu1Nq1axUbG6s2bdqoSZMmLm0vnTL29fXVbbfdprNnz6pmzZq69957FRsbq9mzZ2vfvn1au3atxo8fr/nz519Tn//Oo5C3bt06DRw4MNPySpUqKT4+/pqLAgAA1jR27FiXU5/XX3+9/vOf/+jzzz9XvXr1NHr0aI0dO9ajU7qXa9Omjfbu3avY2FjVrl1bt912m+Lj4/XDDz9ke7OGO2w2m77++muVKlVKN998s6Kjo1W1alV98cUXWbYPCgrS999/L2OM7rjjDiUlJWnatGmKjY3V0KFDVatWLXXu3Fnr1q1T5cqVPa4ry1qNB9NNly9fXgsXLlTjxo1VokQJbdmyRVWrVtWiRYs0YMAAHTp0KFeLzE2JiYkKCQnR6dOnnefHActLTZLGXbyLS88czvVn1yanXnDe6efp3XcAgNzl0UjeXXfdpbFjxzpnjbbZbDp48KCGDx+ubt265WqBAAAAyDmPQt5rr72ms2fPqly5ckpJSVGbNm1UvXp1lShRItOs0AAAAMh/Hp1TCQkJ0aJFi7Ry5Upt2bJFZ8+e1fXXX6/o6Ojcrg8AAAAeyHHIy8jI0PTp0zV79mzt379fNptNVapUUVhYmIwxzHcFAABQAOTodK0xRnfddZfuv/9+/fnnn6pfv77q1q2rAwcOqF+/furSpUte1QkAAIAcyNFI3vTp0/Xzzz9r8eLFateuncu6n376SZ07d9bHH3+s2NjYXC0SAAAAOZOjkbzPPvtMzzzzTKaAJ0n/+Mc/9PTTT+vTTz/NteIAAADgmRyFvK1bt6pDhw7Zrr/tttu0ZcuWay4KAAAA1yZHIe/EiRMKDQ3Ndn1oaKhOnjx5zUUBAADg2uQo5KWnp8vXN/vL+Hx8fHThwoVrLgoAAMBTNptNc+fO9XYZXpejGy+MMerXr5/8/f2zXH/+/PlcKQoAABR+x44d0+jRozV//nwlJCSoVKlSatiwoUaPHq1WrVp5uzzLy1HI69u371XbcGctAACQpG7duik1NVUzZsxQ1apVlZCQoMWLF+uvv/7ydmk5kpqaKrvd7u0ycixHIW/atGl5VQcAALCQU6dOafny5Vq6dKnatGkjSYqIiFDTpk2dbWw2m+Li4vTNN9/op59+UkREhKZOnapy5crp/vvv17p169SwYUN98sknqlatmnO79957TxMnTtShQ4dUpUoVjRw5Un369Mm2ljFjxmjKlClauHChGjRooBUrVmjEiBFav369ypYtqy5dumj8+PEqXry4JCkyMlL33Xefdu3apblz56pr166aPn163rxRecijZ9cCAAAvMkZKTcr/lzFulxgUFKSgoCDNnTv3ipdzvfDCC4qNjdXmzZtVu3Zt9e7dWwMHDnSGMGOMHnvsMWf7OXPmaNCgQRo6dKh+++03DRw4UP3799eSJUuyeJuMHn/8cX388cdavny5GjRooD179qhDhw7q1q2btm7dqi+++EIrVqxwOYYkTZw4UQ0bNtSmTZs0atQot/tdkNiMycF3zAISExMVEhKi06dPKzg42NvlAPkjNUkaV/Hiv585LNmL5+ruk1MvKGr0QknStrExctg9eiw2AHdd/pnOTzn8/fHVV1/pgQceUEpKiq6//nq1adNGd999txo0aCDp4kjeyJEj9cILL0iSfvnlF7Vo0UIfffSRBgwYIEn6/PPP1b9/f6WkpEiSWrVqpbp162rKlCnO4/Ts2VNJSUmaP3++c7+zZs3SnDlztGnTJi1atEiVKlWSJN1///3y8fHR+++/79x+xYoVatOmjZKSkhQQEKDIyEg1btxYc+bMuYY3y/sYyQMAAHmiW7duOnz4sObNm6cOHTpo6dKluv76611OfV4KfJKc07TVr1/fZdm5c+eUmJgoSdq+fXummzZatWql7du3uywbPHiw1qxZo59//tkZ8CRpy5Ytmj59unOkMSgoSDExMcrIyNC+ffuc7Zo0aXLtb4CX8d9tAAAKGz/HxVE1bxw3hwICAtS+fXu1b99eo0aN0v33368xY8aoX79+F3fp5+dsa7PZsl2WkZGRo+O2b99en332mRYuXKh7773Xufzs2bMaOHCgnnjiiUzbVK5c2fnvS9fnFWaEPAAAChubLdcvu8gvUVFR1zSHXZ06dbRy5UqXGT9WrlypqKgol3Z33XWXOnbsqN69e8vHx0d33323JOn666/Xtm3bVL16dY9rKCwIeQAAINf99ddf6tGjhwYMGKAGDRqoRIkSWr9+vV555RV16tTJ4/0+9dRT6tmzpxo3bqzo6Gh98803mj17tn788cdMbbt06aJPPvlEffr0ka+vr7p3767hw4erefPmeuyxx3T//ferePHi2rZtmxYtWqS33377Wrpc4BDyAABArgsKClKzZs30+uuva8+ePUpLS1N4eLgeeOABPfPMMx7vt3PnznrjjTc0ceJEDRo0SFWqVNG0adPUtm3bLNt3795dGRkZ6tOnj4oVK6auXbtq2bJlevbZZ3XTTTfJGKNq1aqpV69eHtdUUHF3LVAUcHctABQ53F0LAABgQfx3GyisjJHSkt1rm+pmOwCAZRDygMLIGGlqjHRojbcrAQAUUJyuBQqjtGTPAl54c4/muQIAFD6M5AGF3bDdkt3N4ObnuDi/FgDA8gh5QGFndxTaSVEBAHmH07UAAAAWRMgDAACwIEIeAACABRHyAAAALKhAhLx33nlHkZGRCggIULNmzbR27Vq3tvv8889ls9nUuXPnvC0QAACgkPF6yPviiy80ZMgQjRkzRhs3blTDhg0VExOjo0ePXnG7/fv3a9iwYbrpppvyqVKgaDHGKDn1gpuvdG+XCwD4G69PoTJp0iQ98MAD6t+/vyQpLi5O8+fP19SpU/X0009nuU16erruvfdePf/881q+fLlOnTqVjxUD1meMUfe41dpw4KS3SwEAeMirI3mpqanasGGDoqOjncuKFSum6OhorV69Otvtxo4dq/Lly+u+++676jHOnz+vxMRElxeAK0tJS/co4DWJKKVAP588qAgAkFNeHck7fvy40tPTFRoa6rI8NDRUf/zxR5bbrFixQh999JE2b97s1jHGjx+v559//lpLBYqs9SOj5bC7F9wC/Xxk44kaAFAgeP2avJw4c+aM+vTpow8++EBly5Z1a5sRI0bo9OnTztehQ4fyuErAWhx2Hznsvm69CHgAUHB4dSSvbNmy8vHxUUJCgsvyhIQEhYWFZWq/Z88e7d+/Xx07dnQuy8jIkCT5+vpqx44dqlatmss2/v7+8vf3z4PqAQAACi6vjuTZ7XbdcMMNWrx4sXNZRkaGFi9erBYtWmRqX7t2bf3666/avHmz83XXXXepXbt22rx5s8LDw/OzfAAAgALL63fXDhkyRH379lWTJk3UtGlTTZ48WUlJSc67bWNjY1WpUiWNHz9eAQEBqlevnsv2JUuWlKRMywEAAIoyr4e8Xr166dixYxo9erTi4+PVqFEjLViwwHkzxsGDB1WsWKG6dBAAAMDrbMYY4+0i8lNiYqJCQkJ0+vRpBQcHe7scwDOpSdK4ihf//cxhyV48V3efnHpBUaMXSpK2jY2Rw+71/w8CAHKIITIAAAALIuQBAABYECEPAADAggh5AAAAFkTIAwAAsCBCHgAAgAUR8gAAACyIya+AgsAYKS3Z/fapOWgLACiSCHmAtxkjTY2RDq3xdiUAAAvhdC3gbWnJnge88OaSnyN36wEAWAIjeUBBMmy3ZM9BaPNzSDZb3tUDACi0CHlAQWJ35PpzaAEARROnawEAACyIkAcAAGBBhDwAAAALIuQBAABYECEPAADAggh5AAAAFkTIAwAAsCBCHgAAgAUR8gAAACyIkAcAAGBBhDwAAAALIuQBAABYECEPAADAggh5AAAAFkTIAwAAsCBCHgAAgAX5ersAAPnDGKOUtHS32ianutcOAFBwEfKAIsAYo+5xq7XhwElvlwIAyCecrgWKgJS0dI8CXpOIUgr088mDigAAeY2RPKCIWT8yWg67e8Et0M9HNpstjysCAOQFQh5QxDjsPnLY+egDgNVxuhYAAMCC+O88kBeMkdKS3Wub6mY7AABygJAH5DZjpKkx0qE13q4EAFCEcboWyG1pyZ4FvPDmkp8j9+sBABRJjOQBeWnYbsnuZnDzc0jcyQoAyCWEPCAv2R2Svbi3qwAAFEGcrgUAALAgQh4AAIAFEfIAAAAsiJAHAABgQYQ8AAAACyLkAQAAWBAhDwAAwIIIeQAAABZEyAMAALAgQh4AAIAFEfIAAAAsiJAHAABgQYQ8AAAACyLkAQAAWBAhDwAAwIIIeQAAABZEyAMAALAgQh4AAIAFEfIAAAAsiJAHAABgQYQ8AAAACyLkAQAAWBAhDwAAwIIIeQAAABZEyAMAALAgX28XAMAzxhilpKW71TY51b12AADrIOQBhZAxRt3jVmvDgZPeLgUAUEBxuhYohFLS0j0KeE0iSinQzycPKgIAFDSM5AGF3PqR0XLY3QtugX4+stlseVwRAKAgIOQBhZzD7iOHnY8yAMAVp2sBAAAsqECEvHfeeUeRkZEKCAhQs2bNtHbt2mzbfvDBB7rppptUqlQplSpVStHR0VdsD+QKY6TUJDdfyd6uFgAA75+u/eKLLzRkyBDFxcWpWbNmmjx5smJiYrRjxw6VL18+U/ulS5fqnnvuUcuWLRUQEKAJEybo1ltv1e+//65KlSp5oQewPGOkqTHSoTXergQAALfZjDHGmwU0a9ZMN954o95++21JUkZGhsLDw/X444/r6aefvur26enpKlWqlN5++23FxsZetX1iYqJCQkJ0+vRpBQcHX3P9KAJSk6RxFXO+XXhzacACKQ9udEhOvaCo0QslSdvGxnBNHgAgE6/+ZUhNTdWGDRs0YsQI57JixYopOjpaq1evdmsfycnJSktLU+nSpbNcf/78eZ0/f975dWJi4rUVjaJt2G7J7nCvrZ8jTwIeAADu8Oo1ecePH1d6erpCQ0NdloeGhio+Pt6tfQwfPlwVK1ZUdHR0luvHjx+vkJAQ5ys8PPya60YRZndI9uLuvQh4AAAvKhA3Xnjq5Zdf1ueff645c+YoICAgyzYjRozQ6dOnna9Dhw7lc5UAAAD5z6una8uWLSsfHx8lJCS4LE9ISFBYWNgVt504caJefvll/fjjj2rQoEG27fz9/eXv758r9QIAABQWXh3Js9vtuuGGG7R48WLnsoyMDC1evFgtWrTIdrtXXnlFL7zwghYsWKAmTZrkR6kAAACFitdvyRsyZIj69u2rJk2aqGnTppo8ebKSkpLUv39/SVJsbKwqVaqk8ePHS5ImTJig0aNHa+bMmYqMjHReuxcUFKSgoCCv9QMAAKAg8XrI69Wrl44dO6bRo0crPj5ejRo10oIFC5w3Yxw8eFDFiv1vwPG9995Tamqqunfv7rKfMWPG6LnnnsvP0gEAAAosr4c8SXrsscf02GOPZblu6dKlLl/v378/7wsCAAAo5Ar13bUAAADIGiEPAADAggh5AAAAFkTIAwAAsCBCHgAAgAUR8gAAACyIkAcAAGBBhDwAAAALIuQBAABYUIF44gVQ1BljlJKW7nb75FT32wIAiiZCHuBlxhh1j1utDQdOersUAICFcLoW8LKUtHSPA16TiFIK9PPJ5YoAAFbASB5QgKwfGS2H3f3QFujnI5vNlocVAQAKK0IeUIA47D5y2PlYAgCuHadrAQAALIiQBwAAYEGEPAAAAAsi5AEAAFgQV3ij6DFGSkt2v31qDtoCAFBAEPJQtBgjTY2RDq3xdiUAAOQpTteiaElL9jzghTeX/By5Ww8AAHmEkTwUXcN2S/YchDY/h8TEwwCAQoKQh6LL7pDsxb1dBQAAeYLTtQAAABZEyAMAALAgQh4AAIAFEfIAAAAsiJAHAABgQdxdC+QBY4xS0tLdapuc6l47AABygpAH5DJjjLrHrdaGAye9XQoAoAjjdC2Qy1LS0j0KeE0iSinQzycPKgIAFEWM5AF5aP3IaDns7gW3QD8f2XiiBgAglxDyUPgZc/GZtO5IdbNdLnHYfeSw8zEDAOQ//vqgcDNGmhojHVrj7UoAAChQuCYPhVtasmcBL7y55OfI/XoAACggGMmDdQzbLdndDG5+Donr3wAAFkbIg3XYHZK9uLerAACgQOB0LQAAgAUR8gAAACyIkAcAAGBBhDwAAAALIuQBAABYEHfXouApgE+wMMYoJS3drbbJqe61AwAgLxHyULAUwCdYGGPUPW61Nhw46e1SAABwG6drUbAUwCdYpKSlexTwmkSUUqCfTx5UBADA1TGSh4KrAD7BYv3IaDns7gW3QD8f2XiqBgDASwh5KLgK4BMsHHYfOex8bAAABR+nawEAACyIkAcAAGBBhDwAAAALIuQBAABYECEPAADAgrhNEEVOTp5eIfEECwBA4UTIQ5HC0ysAAEUFp2tRpHj69AqJJ1gAAAoXRvKQ94y5+Lgyd6S62S4X5OTpFRJPsAAAFC6EPOQtY6SpMZ49j9btQ7h/jd3l19fx9AoAgJXxFw55Ky3Zs4AX3vzi82ivgmvsAADIGiEP+WfY7ovPo3WHn0Ny49Sop9fYcX0dAMDqCHnIP3aHZC+eZ7vPyTV2XF8HALA6Qh5yJic3UUj5eiMF19gBAPA//EWE+/LhJoqLh/HsRgoAAPA/hDy4z9ObKCRupAAAIJ8R8oo6T+ewy8lNFBI3UgAAkM8IeUXZtZx+zeObKCRupAAA4FoQ8oqyPJ7D7hImKwYAIP/xFxQXuXn61RijFPlLboY2Y6Qecau17UjitVYIAABygJBnNZ5eY+fG6df8vCmCa+wAALg2hDwr8eAaO2N0cWQuNV3ShSu2TU717KYISYqqEKxZD7Vw594LSVxjBwDAtSLkWUlasszBNRdDmxuMpB6pY7TNREovLs/RoXJyU4REaAMAIL8R8izEGKPuqWO0wdTK0+M0iSilMsXthDYAAAqwAhHy3nnnHb366quKj49Xw4YN9dZbb6lp06bZtp81a5ZGjRql/fv3q0aNGpowYYJuv/32fKzYMzm5y/T/N5DSUtxunpx01qOAx6lUAACsx+sh74svvtCQIUMUFxenZs2aafLkyYqJidGOHTtUvnz5TO1XrVqle+65R+PHj9edd96pmTNnqnPnztq4caPq1auXb3WbjAylJJ91v70x6vHRZm2Ld3+ba7H+Xy3lCCrhVltCGwAA1mMzxhhvFtCsWTPdeOONevvttyVJGRkZCg8P1+OPP66nn346U/tevXopKSlJ3377rXNZ8+bN1ahRI8XFxV31eImJiQoJCdHp06cVHBzscd3JZxMVlcPr2PJLk4DDmjX6PtmKFfN2KQAAwEu8OpKXmpqqDRs2aMSIEc5lxYoVU3R0tFavXp3lNqtXr9aQIUNclsXExGju3LlZtj9//rzOnz/v/Pr06dOSLoa9a5F8NlEZ592cquQytWwH9LH9Zbk9blaujnT3TLceCXZJoKOVzpzNnxFDAMBFJUqU4KwIChSvhrzjx48rPT1doaGhLstDQ0P1xx9/ZLlNfHx8lu3j4+OzbD9+/Hg9//zzmZaHh4d7WPW1OSSpYo62WCENq5w3xQAAcs21niECcpvXr8nLayNGjHAZ+cvIyNCJEydUpkwZr/6PKzExUeHh4Tp06FCR+aVAn+mzVdFn+ixdHMkDChKvhryyZcvKx8dHCQkJLssTEhIUFhaW5TZhYWE5au/v7y9/f9d540qWLOl50bksODi4yPyCvIQ+Fw30uWigz0DB5dUr8+12u2644QYtXrzYuSwjI0OLFy9WixYtstymRYsWLu0ladGiRdm2BwAAKIq8frp2yJAh6tu3r5o0aaKmTZtq8uTJSkpKUv/+/SVJsbGxqlSpksaPHy9JGjRokNq0aaPXXntNd9xxhz7//HOtX79eU6ZM8WY3AAAAChSvh7xevXrp2LFjGj16tOLj49WoUSMtWLDAeXPFwYMHVeyyqUBatmypmTNnauTIkXrmmWdUo0YNzZ07N1/nyMsN/v7+GjNmTKZTyVZGn4sG+lw00Geg4PP6PHkAAADIfcyWCwAAYEGEPAAAAAsi5AEAAFgQIQ8AAMCCCHl57M8//9Q///lPlSlTRoGBgapfv77Wr1/vXG+M0ejRo1WhQgUFBgYqOjpau3bt8mLF1yY9PV2jRo1SlSpVFBgYqGrVqumFF17Q5ff3FPY+//zzz+rYsaMqVqwom82W6bnJ7vTvxIkTuvfeexUcHKySJUvqvvvu09kC/LzhK/U5LS1Nw4cPV/369VW8eHFVrFhRsbGxOnz4sMs+rNTnv3vooYdks9k0efJkl+VW7PP27dt11113KSQkRMWLF9eNN96ogwcPOtefO3dOjz76qMqUKaOgoCB169Yt0wT2BcnV+nz27Fk99thjuu666xQYGKioqCjFxcW5tClsfUbRQcjLQydPnlSrVq3k5+en77//Xtu2bdNrr72mUqVKOdu88sorevPNNxUXF6c1a9aoePHiiomJ0blz57xYuecmTJig9957T2+//ba2b9+uCRMm6JVXXtFbb73lbFPY+5yUlKSGDRvqnXfeyXK9O/2799579fvvv2vRokX69ttv9fPPP+vBBx/Mry7k2JX6nJycrI0bN2rUqFHauHGjZs+erR07duiuu+5yaWelPl9uzpw5+uWXX1SxYuanUlutz3v27FHr1q1Vu3ZtLV26VFu3btWoUaMUEBDgbDN48GB98803mjVrlpYtW6bDhw+ra9eu+dWFHLtan4cMGaIFCxbo3//+t7Zv364nn3xSjz32mObNm+dsU9j6jCLEIM8MHz7ctG7dOtv1GRkZJiwszLz66qvOZadOnTL+/v7ms88+y48Sc90dd9xhBgwY4LKsa9eu5t577zXGWK/PksycOXOcX7vTv23bthlJZt26dc4233//vbHZbObPP//Mt9o99fc+Z2Xt2rVGkjlw4IAxxrp9/u9//2sqVapkfvvtNxMREWFef/115zor9rlXr17mn//8Z7bbnDp1yvj5+ZlZs2Y5l23fvt1IMqtXr86rUnNNVn2uW7euGTt2rMuy66+/3jz77LPGmMLfZ1gbI3l5aN68eWrSpIl69Oih8uXLq3Hjxvrggw+c6/ft26f4+HhFR0c7l4WEhKhZs2ZavXq1N0q+Zi1bttTixYu1c+dOSdKWLVu0YsUK3XbbbZKs2efLudO/1atXq2TJkmrSpImzTXR0tIoVK6Y1a9bke8154fTp07LZbM7nRFuxzxkZGerTp4+eeuop1a1bN9N6q/U5IyND8+fPV82aNRUTE6Py5curWbNmLqc3N2zYoLS0NJef/9q1a6ty5cqF9vPdsmVLzZs3T3/++aeMMVqyZIl27typW2+9VZI1+wzrIOTlob179+q9995TjRo1tHDhQj388MN64oknNGPGDElSfHy8JDmf7nFJaGioc11h8/TTT+vuu+9W7dq15efnp8aNG+vJJ5/UvffeK8mafb6cO/2Lj49X+fLlXdb7+vqqdOnSlngPzp07p+HDh+uee+5xPsTdin2eMGGCfH199cQTT2S53mp9Pnr0qM6ePauXX35ZHTp00A8//KAuXbqoa9euWrZsmaSLfbbb7c5wf0lh/ny/9dZbioqK0nXXXSe73a4OHTronXfe0c033yzJmn2GdXj9sWZWlpGRoSZNmmjcuHGSpMaNG+u3335TXFyc+vbt6+Xq8sZ//vMfffrpp5o5c6bq1q2rzZs368knn1TFihUt22f8T1pamnr27CljjN577z1vl5NnNmzYoDfeeEMbN26UzWbzdjn5IiMjQ5LUqVMnDR48WJLUqFEjrVq1SnFxcWrTpo03y8szb731ln755RfNmzdPERER+vnnn/Xoo4+qYsWKLqN3QEHESF4eqlChgqKiolyW1alTx3knWlhYmCRlugsrISHBua6weeqpp5yjefXr11efPn00ePBgjR8/XpI1+3w5d/oXFhamo0ePuqy/cOGCTpw4Uajfg0sB78CBA1q0aJFzFE+yXp+XL1+uo0ePqnLlyvL19ZWvr68OHDigoUOHKjIyUpL1+ly2bFn5+vpe9XdaamqqTp065dKmsH6+U1JS9Mwzz2jSpEnq2LGjGjRooMcee0y9evXSxIkTJVmvz7AWQl4eatWqlXbs2OGybOfOnYqIiJAkValSRWFhYVq8eLFzfWJiotasWaMWLVrka625JTk5WcWKuf5Y+fj4OEcBrNjny7nTvxYtWujUqVPasGGDs81PP/2kjIwMNWvWLN9rzg2XAt6uXbv0448/qkyZMi7rrdbnPn36aOvWrdq8ebPzVbFiRT311FNauHChJOv12W6368Ybb7zi77QbbrhBfn5+Lj//O3bs0MGDBwvl5zstLU1paWlX/J1mtT7DYrx954eVrV271vj6+pqXXnrJ7Nq1y3z66afG4XCYf//73842L7/8silZsqT5+uuvzdatW02nTp1MlSpVTEpKihcr91zfvn1NpUqVzLfffmv27dtnZs+ebcqWLWv+9a9/OdsU9j6fOXPGbNq0yWzatMlIMpMmTTKbNm1y3knqTv86dOhgGjdubNasWWNWrFhhatSoYe655x5vdemqrtTn1NRUc9ddd5nrrrvObN682Rw5csT5On/+vHMfVupzVv5+d60x1uvz7NmzjZ+fn5kyZYrZtWuXeeutt4yPj49Zvny5cx8PPfSQqVy5svnpp5/M+vXrTYsWLUyLFi281aWrulqf27RpY+rWrWuWLFli9u7da6ZNm2YCAgLMu+++69xHYeszig5CXh775ptvTL169Yy/v7+pXbu2mTJlisv6jIwMM2rUKBMaGmr8/f3NLbfcYnbs2OGlaq9dYmKiGTRokKlcubIJCAgwVatWNc8++6zLH/vC3uclS5YYSZleffv2Nca417+//vrL3HPPPSYoKMgEBweb/v37mzNnznihN+65Up/37duX5TpJZsmSJc59WKnPWckq5Fmxzx999JGpXr26CQgIMA0bNjRz58512UdKSop55JFHTKlSpYzD4TBdunQxR44cyeeeuO9qfT5y5Ijp16+fqVixogkICDC1atUyr732msnIyHDuo7D1GUWHzZjLHkUAAAAAS+CaPAAAAAsi5AEAAFgQIQ8AAMCCCHkAAAAWRMgDAACwIEIeAACABRHyAAAALIiQBxQxbdu21ZNPPuntMgAAeYyQB8Bjzz33nBo1auTtMgAAWSDkAQAAWBAhDyiCLly4oMcee0whISEqW7asRo0aJWOM3n77bdWrV8/Zbu7cubLZbIqLi3Mui46O1siRIzV9+nQ9//zz2rJli2w2m2w2m6ZPn+6F3gAAskLIA4qgGTNmyNfXV2vXrtUbb7yhSZMm6cMPP1SbNm20bds2HTt2TJK0bNkylS1bVkuXLpUkpaWlafXq1Wrbtq169eqloUOHqm7dujpy5IiOHDmiXr16ebFXAIDL+Xq7AAD5Lzw8XK+//rpsNptq1aqlX3/9Va+//rp+//13lS5dWsuWLVP37t21dOlSDR06VG+88YYkae3atUpLS1PLli0VGBiooKAg+fr6KiwszMs9AgD8HSN5QBHUvHlz2Ww259ctWrTQrl27lJGRoZtvvllLly7VqVOntG3bNj3yyCM6f/68/vjjDy1btkw33nijHA6HF6sHALiDkAfARdu2bbV06VItX75cjRs3VnBwsDP4LVu2TG3atPF2iQAANxDygCJozZo1Ll//8ssvqlGjhnx8fJzX5c2aNUtt27aVdDH4/fjjj1q5cqVzmSTZ7Xalp6fnY+UAAHcR8oAi6ODBgxoyZIh27Nihzz77TG+99ZYGDRokSWrQoIFKlSqlmTNnuoS8uXPn6vz582rVqpVzP5GRkdq3b582b96s48eP6/z5897oDgAgC4Q8oAiKjY1VSkqKmjZtqkcffVSDBg3Sgw8+KEmy2Wy66aabZLPZ1Lp1a0kXg19wcLCaNGmi4sWLO/fTrVs3dejQQe3atVO5cuX02WefeaU/AIDMbMYY4+0iAAAAkLsYyQMAALAgQh4AAIAFEfIAAAAsiJAHAABgQYQ8AAAACyLkAQAAWBAhDwAAwIIIeQAAABZEyAMAALAgQh4AAIAFEfIAAAAsiJAHAABgQf8Hh8ylu4q/nMUAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["sbn.boxplot(data=df, x='Smoking Status', y='bwt', hue='Smoking Status' ,palette={'Non-Smoker': 'skyblue', 'Smoker': 'salmon'}, zorder=0)\n","sbn.stripplot(data=df, x='Smoking Status', y='bwt', color='black', alpha=0.3, zorder=1)\n","sbn.violinplot(data=df, x=\"Smoking Status\", y='bwt', color='blue', alpha=0.3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"id":"_pULfvjJV34J","executionInfo":{"status":"ok","timestamp":1770812033968,"user_tz":-330,"elapsed":381,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"95887988-5443-4805-ef48-3e3b344a6a1f"},"execution_count":73,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<Axes: xlabel='Smoking Status', ylabel='bwt'>"]},"metadata":{},"execution_count":73},{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### 1.4. Statistics Table\n","Finding Skewness and Kurtosis for Smokers and Non Smokers"],"metadata":{"id":"BC3IDV1Ql-n1"}},{"cell_type":"code","source":["print(\"Population Count of Smokers and non smokers: \", df[df['smoke'] ==1].size, df[df['smoke'] ==0].size)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JS2kjmj9f_OP","executionInfo":{"status":"ok","timestamp":1770812033975,"user_tz":-330,"elapsed":5,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"1263549b-20bf-4e34-c52b-1790de3ddf73"},"execution_count":74,"outputs":[{"output_type":"stream","name":"stdout","text":["Population Count of Smokers and non smokers:  1936 2968\n"]}]},{"cell_type":"code","source":["from scipy.stats import kurtosis, skew\n","\n","stats_table = df.groupby('smoke')['bwt'].agg([ 'mean', 'var', 'skew', lambda x: kurtosis(x, fisher=True)]).reset_index()\n","\n","stats_table.columns = ['Smoking Status', 'Mean', 'Variance', 'Skewness', 'Excess Kurtosis']\n","stats_table.round(2)"],"metadata":{"id":"tqS9WHWJna46","colab":{"base_uri":"https://localhost:8080/","height":112},"executionInfo":{"status":"ok","timestamp":1770812033981,"user_tz":-330,"elapsed":5,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"7e029672-c51b-4063-a22c-c0b2bbf8974f"},"execution_count":75,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Smoking Status    Mean  Variance  Skewness  Excess Kurtosis\n","0               0  123.05    302.71     -0.19             1.04\n","1               1  114.11    327.57     -0.03            -0.01"],"text/html":["\n","  <div id=\"df-7893abe2-f209-4061-b992-5a6ae89767d9\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Smoking Status</th>\n","      <th>Mean</th>\n","      <th>Variance</th>\n","      <th>Skewness</th>\n","      <th>Excess Kurtosis</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>123.05</td>\n","      <td>302.71</td>\n","      <td>-0.19</td>\n","      <td>1.04</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>114.11</td>\n","      <td>327.57</td>\n","      <td>-0.03</td>\n","      <td>-0.01</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7893abe2-f209-4061-b992-5a6ae89767d9')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-7893abe2-f209-4061-b992-5a6ae89767d9 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Inference:\n","\n","Baby weight of non smoking mothers are on average weigh **9 ounces** more than the smoking counterpart.\n","\n","Baby weight under smoking category seems to be very symmetrical (almost 0 skewness and excess kurtosis), like a Gaussian curve. Meanwhile, under non-smoking category is skewed towards left with fatter tails (*Leptokurtic*), leading to a greater possibility of extreme cases.\n","\n","Overall, the significant centre of mass shift of the curve by 9 ounces shows that **Smoking might affect baby's weight negatively**."],"metadata":{"id":"PxG5q89i-pzl"}},{"cell_type":"markdown","source":["##2. Part B: Video Game"],"metadata":{"id":"wHzqUDcVCoX7"}},{"cell_type":"markdown","source":["###2.1. Reading Data"],"metadata":{"id":"9KMY8JQnFFMC"}},{"cell_type":"code","source":["filepath_a = \"/content/drive/MyDrive/Colab Notebooks/EE3111_lab2_b.data\"\n","\n","df = pd.read_csv(filepath_a, sep=r'\\s+', engine='python') # Here '\\s+' is a regex used for capturing all spaces\n","df = df[df['time'] > 0]\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"WMXaVaVt-lK0","executionInfo":{"status":"ok","timestamp":1770812033990,"user_tz":-330,"elapsed":9,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"46ae67cc-ba16-40de-c9ae-289efa5f654e"},"execution_count":76,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    time  like  where  freq  busy  educ  sex  age  home  math  work  own  \\\n","0    2.0     3      3     2     0     1    0   19     1     0    10    1   \n","3    0.5     3      3     3     0     1    0   19     1     0     0    1   \n","8    2.0     3      2     1     1     1    1   19     0     0     0    0   \n","13   3.0     3      3     2     1     0    0   18     0     0     0    0   \n","14   1.0     3      5     2     0     1    0   18     1     1    14    1   \n","\n","    cdrom  email  grade  \n","0       0      1      4  \n","3       0      1      3  \n","8       0      0      4  \n","13      0      1      3  \n","14      0      1      3  "],"text/html":["\n","  <div id=\"df-93890534-82c0-4f5d-a045-e3b7cbca493e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>time</th>\n","      <th>like</th>\n","      <th>where</th>\n","      <th>freq</th>\n","      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 \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 0,\n        \"max\": 99,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"email\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":76}]},{"cell_type":"code","source":["df.size"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1_ToKG40Eedk","executionInfo":{"status":"ok","timestamp":1770812034000,"user_tz":-330,"elapsed":9,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"af8e667c-e976-4b7a-d9ad-7b81351bc986"},"execution_count":77,"outputs":[{"output_type":"execute_result","data":{"text/plain":["510"]},"metadata":{},"execution_count":77}]},{"cell_type":"markdown","source":["###2.2. MOM Estimator\n","\n","E[X] = alpha beta\n","\n","Var = alpha * beta**2"],"metadata":{"id":"56BZV684FdIa"}},{"cell_type":"code","source":["mean = df['time'].mean()\n","var = df['time'].var()\n","\n","print(\"Mean: \", mean)\n","print(\"Variance: \", var)\n","\n","mom_beta = var / mean\n","mom_alpha = mean / mom_beta\n","\n","print(\"MOM Alpha: \", mom_alpha)\n","print(\"MOM Beta: \", mom_beta)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nl6gSm4fFctR","executionInfo":{"status":"ok","timestamp":1770812034001,"user_tz":-330,"elapsed":2,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"07449a99-6bb8-4913-8361-79d451762c0d"},"execution_count":78,"outputs":[{"output_type":"stream","name":"stdout","text":["Mean:  3.326470588235294\n","Variance:  31.766247771836024\n","MOM Alpha:  0.3483384834706559\n","MOM Beta:  9.549535139190317\n"]}]},{"cell_type":"markdown","source":["###2.3. Self ML Estimator\n","\n","Since it contains digamma function, we can't do it analytically. We can use Netwon-Raphson methods to find alpha and beta\n","\n","By differentiating ln(L(alpha, beta)), we get\n","\n"," - alpha * beta = sum(Xi)/n\n","\n"," - ln(alpha) - digamma(alpha) = ln(sum(Xi)/n) - sum(ln(Xi))/n"],"metadata":{"id":"PoclGE5dHtYa"}},{"cell_type":"code","source":["import numpy as np\n","from scipy.special import polygamma\n","\n","data = df['time'].values\n","k = np.log(np.mean(data)) - np.mean(np.log(data))\n","\n","alpha_mle = mom_alpha\n","\n","for i in range(10):  # It converged fast\n","    f_alpha = np.log(alpha_mle) - polygamma(0, alpha_mle) - k\n","    f_prime_alpha = (1/alpha_mle) - polygamma(1, alpha_mle) # (0 - yold) / (xnew - xold) = dy/ dx\n","\n","    alpha_mle = alpha_mle - (f_alpha / f_prime_alpha)\n","    print(f\"{i+1}: alpha = {alpha_mle:.6f}\")\n","\n","beta_mle = np.mean(data) / alpha_mle\n","print(f\"\\nMLE: Alpha = {alpha_mle:.4f}, Beta = {beta_mle:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MmHh1bE9Hrwy","executionInfo":{"status":"ok","timestamp":1770812034013,"user_tz":-330,"elapsed":12,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"36231cd6-fe99-4b84-e43b-1302396c7bb5"},"execution_count":79,"outputs":[{"output_type":"stream","name":"stdout","text":["1: alpha = 0.549593\n","2: alpha = 0.761275\n","3: alpha = 0.890247\n","4: alpha = 0.918727\n","5: alpha = 0.919757\n","6: alpha = 0.919758\n","7: alpha = 0.919758\n","8: alpha = 0.919758\n","9: alpha = 0.919758\n","10: alpha = 0.919758\n","\n","MLE: Alpha = 0.9198, Beta = 3.6167\n"]}]},{"cell_type":"markdown","source":["###2.4. MLE Scipy"],"metadata":{"id":"D2hpwuUnMmVx"}},{"cell_type":"code","source":["from scipy.stats import gamma\n","\n","alpha_mle, loc_mle, beta_mle = gamma.fit(df['time'], floc=0) #Locking the location parameter to 0\n","\n","print(f\"MLE Estimates: Alpha = {alpha_mle:.4f}, Beta = {beta_mle:.4f}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V0KyYBWhEgst","executionInfo":{"status":"ok","timestamp":1770812034016,"user_tz":-330,"elapsed":2,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"e23e143c-fe31-4037-b17e-486bd2c46fe4"},"execution_count":80,"outputs":[{"output_type":"stream","name":"stdout","text":["MLE Estimates: Alpha = 0.9198, Beta = 3.6167\n"]}]},{"cell_type":"markdown","source":["###2.5. Visual Fit"],"metadata":{"id":"SiVYjlvCR-86"}},{"cell_type":"code","source":["x = np.linspace(0, df['time'].max())\n","\n","plt.hist(df['time'], bins=50, density=True, alpha=0.3)\n","plt.plot(x, gamma.pdf(x,mom_alpha, scale=mom_beta), 'b--', label='MOM')\n","plt.plot(x, gamma.pdf(x, alpha_mle, scale=beta_mle), 'r-', label='MLE')\n","plt.legend()\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"id":"VfLUc_22SA4I","executionInfo":{"status":"ok","timestamp":1770812034228,"user_tz":-330,"elapsed":212,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"3598b3b2-b6bb-40db-a5a3-8e676bed1177"},"execution_count":81,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["###2.6. Inference\n","\n","MLE Red Line, seems to fit the peak well when compared to MOM. The reason because, MOM can get shifted due to outliers (like 15 and 30 here), while MLE optimises the likelihood the occurence of the datapoints. Considering lot of datapoints are at the peak, MLE fits it well."],"metadata":{"id":"UHeDqBBSUuf4"}},{"cell_type":"markdown","source":["###2.7. In Class"],"metadata":{"id":"Hqj74XxUnKvx"}},{"cell_type":"code","source":["# Checking Log Likelihood of mle, mom\n","from scipy.stats import gamma, kstest\n","\n","def log_likelihood(data, alpha, beta):\n","    # Scipy gamma uses (a, scale=beta)\n","    return np.sum(gamma.logpdf(data, a=alpha, scale=beta))\n","\n","ll_mle = log_likelihood(data, alpha_mle, beta_mle)\n","ll_mom = log_likelihood(data, mom_alpha, mom_beta)\n","\n","ks_ml = kstest(data, 'gamma', args=(alpha_mle, 0, beta_mle)).statistic\n","\n","ks_mom = kstest(data, 'gamma', args=(mom_alpha, 0, mom_beta)).statistic\n"],"metadata":{"id":"GmAZOxpAk8uE","executionInfo":{"status":"ok","timestamp":1770812034232,"user_tz":-330,"elapsed":2,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}}},"execution_count":82,"outputs":[]},{"cell_type":"code","source":["ll_mle, ll_mom, ks_ml, ks_mom"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Iza_siuSlBB6","executionInfo":{"status":"ok","timestamp":1770812034263,"user_tz":-330,"elapsed":29,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"c72ae809-e8a0-4e39-c28b-3cdd1ee9121c"},"execution_count":83,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(np.float64(-74.78531787658886),\n"," np.float64(-83.09727632497773),\n"," np.float64(0.2993794844176108),\n"," np.float64(0.3667873947541731))"]},"metadata":{},"execution_count":83}]},{"cell_type":"markdown","source":["Inference: LL_MLE > LL_MOM"],"metadata":{"id":"yQDSZdOPnTp0"}},{"cell_type":"code","source":["# Define the ranges you want to test\n","alpha_values = [1.0, 1.5, 2.0, 2.5]\n","beta_values = [0.5, 1.0, 2.0, 3.0]\n","\n","results = []\n","\n","for a in alpha_values:\n","    for b in beta_values:\n","        ll = log_likelihood(data, a, b)\n","\n","        ks = kstest(data, 'gamma', args=(a, 0, b)).statistic\n","\n","        results.append({\n","            'Alpha': a,\n","            'Beta': b,\n","            'Log-Likelihood': ll,\n","            'KS Statistic': ks\n","        })\n","\n","results_df = pd.DataFrame(results)\n","\n","results_df.sort_values(by=\"Log-Likelihood\", ascending=False)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":551},"id":"jfc-gTwMlHEs","executionInfo":{"status":"ok","timestamp":1770812034336,"user_tz":-330,"elapsed":57,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}},"outputId":"0cc5baaf-c80a-4fe4-d4f4-c877844c36b1"},"execution_count":84,"outputs":[{"output_type":"execute_result","data":{"text/plain":["    Alpha  Beta  Log-Likelihood  KS Statistic\n","3     1.0   3.0      -75.052818      0.278123\n","6     1.5   2.0      -78.137867      0.337113\n","7     1.5   3.0      -79.966588      0.485939\n","2     1.0   2.0      -80.117004      0.279179\n","10    2.0   2.0      -84.371922      0.500465\n","11    2.0   3.0      -93.093550      0.620401\n","9     2.0   1.0      -93.787914      0.241053\n","13    2.5   1.0      -93.811089      0.314122\n","14    2.5   2.0      -96.178599      0.613851\n","5     1.5   1.0      -99.337361      0.385595\n","15    2.5   3.0     -111.793133      0.702086\n","1     1.0   1.0     -113.100000      0.511724\n","12    2.5   0.5     -147.993579      0.490823\n","8     2.0   0.5     -159.753906      0.555481\n","4     1.5   0.5     -177.086855      0.601047\n","0     1.0   0.5     -202.632996      0.688194"],"text/html":["\n","  <div id=\"df-0aa91a4e-b4d4-48a9-abd5-91a4e9ac038f\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Alpha</th>\n","      <th>Beta</th>\n","      <th>Log-Likelihood</th>\n","      <th>KS Statistic</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>3</th>\n","      <td>1.0</td>\n","      <td>3.0</td>\n","      <td>-75.052818</td>\n","      <td>0.278123</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>1.5</td>\n","      <td>2.0</td>\n","      <td>-78.137867</td>\n","      <td>0.337113</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>1.5</td>\n","      <td>3.0</td>\n","      <td>-79.966588</td>\n","      <td>0.485939</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>-80.117004</td>\n","      <td>0.279179</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>2.0</td>\n","      <td>2.0</td>\n","      <td>-84.371922</td>\n","      <td>0.500465</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>2.0</td>\n","      <td>3.0</td>\n","      <td>-93.093550</td>\n","      <td>0.620401</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>-93.787914</td>\n","      <td>0.241053</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>2.5</td>\n","      <td>1.0</td>\n","      <td>-93.811089</td>\n","      <td>0.314122</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>2.5</td>\n","      <td>2.0</td>\n","      <td>-96.178599</td>\n","      <td>0.613851</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>1.5</td>\n","      <td>1.0</td>\n","      <td>-99.337361</td>\n","      <td>0.385595</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>2.5</td>\n","      <td>3.0</td>\n","      <td>-111.793133</td>\n","      <td>0.702086</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>-113.100000</td>\n","      <td>0.511724</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>2.5</td>\n","      <td>0.5</td>\n","      <td>-147.993579</td>\n","      <td>0.490823</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>2.0</td>\n","      <td>0.5</td>\n","      <td>-159.753906</td>\n","      <td>0.555481</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>1.5</td>\n","      <td>0.5</td>\n","      <td>-177.086855</td>\n","      <td>0.601047</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>0.5</td>\n","      <td>-202.632996</td>\n","      <td>0.688194</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0aa91a4e-b4d4-48a9-abd5-91a4e9ac038f')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","  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MLE does not optimise KS."],"metadata":{"id":"m_9tIYoJnZk-"}},{"cell_type":"code","source":[],"metadata":{"id":"FDsGLINUlcMr","executionInfo":{"status":"ok","timestamp":1770812034337,"user_tz":-330,"elapsed":1,"user":{"displayName":"Fahd B ee24b016","userId":"03823553353367620639"}}},"execution_count":84,"outputs":[]}]}